Title
On Optimal Quantization Rules for Some Problems in Sequential Decentralized Detection
Abstract
We consider the design of systems for sequential decentralized detection, a problem that entails several interdependent choices: the choice of a stopping rule (specifying the sample size), a global decision function (a choice between two competing hypotheses), and a set of quantization rules (the local decisions on the basis of which the global decision is made). This correspondence addresses an open problem of whether in the Bayesian formulation of sequential decentralized detection, optimal local decision functions can be found within the class of stationary rules. We develop an asymptotic approximation to the optimal cost of stationary quantization rules and exploit this approximation to show that stationary quantizers are not optimal in a broad class of settings. We also consider the class of blockwise-stationary quantizers, and show that asymptotically optimal quantizers are likelihood-based threshold rules.
Year
DOI
Venue
2008
10.1109/TIT.2008.924647
IEEE Transactions on Information Theory
Keywords
Field
DocType
sequential analysis,cost function,hypothesis testing,information theory,statistical computing,quantization,decision theory,sample size,random variables,signal detection,bayesian methods,experimental design,statistics
Mathematical optimization,Open problem,Optimal decision,Detection theory,Computer science,Decision support system,Decision theory,Quantization (signal processing),Asymptotically optimal algorithm,Statistical hypothesis testing
Journal
Volume
Issue
ISSN
54
7
0018-9448
Citations 
PageRank 
References 
5
0.56
7
Authors
3
Name
Order
Citations
PageRank
XuanLong Nguyen141631.22
Martin J. Wainwright27398533.01
Michael I. Jordan3312203640.80